Salaswind8470

Miniprotein binders hold a great interest as a class of drugs that bridges the gap between monoclonal antibodies and small molecule drugs. Like monoclonal antibodies, they can be designed to bind to therapeutic targets with high affinity, but they are more stable and easier to produce and to administer. In this chapter, we present a structure-based computational generic approach for miniprotein inhibitor design. Specifically, we describe step-by-step the implementation of the approach for the design of miniprotein binders against the SARS-CoV-2 coronavirus, using available structural data on the SARS-CoV-2 spike receptor binding domain (RBD) in interaction with its native target, the human receptor ACE2. Structural data being increasingly accessible around many protein-protein interaction systems, this method might be applied to the design of miniprotein binders against numerous therapeutic targets. The computational pipeline exploits provable and deterministic artificial intelligence-based protein design methods, with some recent additions in terms of binding energy estimation, multistate design and diverse library generation.Computational peptide design is useful for therapeutics, diagnostics, and vaccine development. To select the most promising peptide candidates, the key is describing accurately the peptide-target interactions at the molecular level. We here review a computational peptide design protocol whose key feature is the use of all-atom explicit solvent molecular dynamics for describing the different peptide-target complexes explored during the optimization. We describe the milestones behind the development of this protocol, which is now implemented in an open-source code called PARCE. We provide a basic tutorial to run the code for an antibody fragment design example. Finally, we describe three additional applications of the method to design peptides for different targets, illustrating the broad scope of the proposed approach.This chapter discusses the theory and application of physics-based free energy methods to estimate protein-peptide binding free energies. It presents a statistical mechanics formulation of molecular binding, which is then specialized in three methodologies (1) alchemical absolute binding free energy estimation with implicit solvation, (2) alchemical relative binding free energy estimation with explicit solvation, and (3) potential of mean force binding free energy estimation. Case studies of protein-peptide binding application taken from the recent literature are discussed for each method.Constrained peptides represent a relatively new class of biologic therapeutics, which have the potential to overcome several limitations of small-molecule drugs, and of designed antibodies. P5091 chemical structure Because of their modest size, the rational design of such peptides is becoming increasingly amenable to computer simulation; multi-microsecond molecular dynamic (MD) simulations are now routinely possible on consumer-grade graphical processors (GPUs). Here, we describe the procedures for performing and analyzing MD simulations of hydrocarbon-stapled peptides using the CHARMM energy function, in isolation and in complex with a binding partner, to investigate their conformational properties and to compute changes in their binding affinity upon mutation.The immune system is constantly protecting its host from the invasion of pathogens and the development of cancer cells. The specific CD8+ T-cell immune response against virus-infected cells and tumor cells is based on the T-cell receptor recognition of antigenic peptides bound to class I major histocompatibility complexes (MHC) at the surface of antigen presenting cells. Consequently, the peptide binding specificities of the highly polymorphic MHC have important implications for the design of vaccines, for the treatment of autoimmune diseases, and for personalized cancer immunotherapy. Evidence-based machine-learning approaches have been successfully used for the prediction of peptide binders and are currently being developed for the prediction of peptide immunogenicity. However, understanding and modeling the structural details of peptide/MHC binding is crucial for a better understanding of the molecular mechanisms triggering the immunological processes, estimating peptide/MHC affinity using universal physics-based approaches, and driving the design of novel peptide ligands. Unfortunately, due to the large diversity of MHC allotypes and possible peptides, the growing number of 3D structures of peptide/MHC (pMHC) complexes in the Protein Data Bank only covers a small fraction of the possibilities. Consequently, there is a growing need for rapid and efficient approaches to predict 3D structures of pMHC complexes. Here, we review the key characteristics of the 3D structure of pMHC complexes before listing databases and other sources of information on pMHC structures and MHC specificities. Finally, we discuss some of the most prominent pMHC docking software.The cPEPmatch approach is a rapid computational methodology for the rational design of cyclic peptides to target desired regions of protein-protein interfaces. The method selects cyclic peptides that structurally match backbone structures of short segments at a protein-protein interface. In a second step, the cyclic peptides act as templates for designed binders by adapting the amino acid side chains to the side chains found in the target complex. A link to access the different tools that comprise the cPEPmatch method and a detailed step-by-step guide is provided. We outline the protocol by following the application to a trypsin protease in complex with the bovine inhibitor protein (BPTI). An extension of our original approach is also presented, where we give a detailed description of the usage of the cPEPmatch methodology focusing on identifying hot regions of protein-protein interfaces prior to the matching. This extension allows one to reduce the amount of evaluated putative cyclic peptides and to specifically design only those that compete with the strongest protein-protein binding regions. It is illustrated by an application to an MHC class I protein complex.Protein-protein interactions play crucial and subtle roles in many biological processes and modifications of their fine mechanisms generally result in severe diseases. Peptide derivatives are very promising therapeutic agents for modulating protein-protein associations with sizes and specificities between those of small compounds and antibodies. For the same reasons, rational design of peptide-based inhibitors naturally borrows and combines computational methods from both protein-ligand and protein-protein research fields. In this chapter, we aim to provide an overview of computational tools and approaches used for identifying and optimizing peptides that target protein-protein interfaces with high affinity and specificity. We hope that this review will help to implement appropriate in silico strategies for peptide-based drug design that builds on available information for the systems of interest.Our published studies on the self- and co-assembly of cyclo-HH peptides demonstrated their capacity to coordinate with Zn(II), their enhanced photoluminescence and their ability to self-encapsulate epirubicin, a chemotherapy drug. Here, we provide a detailed description of computational and experimental methodology for the study of cyclo-HH self- and co-assembling mechanisms, photoluminescence, and drug encapsulation properties. We outline the experimental protocols, which involve fluorescence spectroscopy, transmission electron microscopy, and atomic force microscopy protocols, as well as the computational protocols, which involve structural and energetic analysis of the assembled nanostructures. We suggest that the computational and experimental methods presented here can be generalizable, and thus can be applied in the investigation of self- and co-assembly systems involving other short peptides, encapsulating compounds and binding to ions, beyond the particular ones presented here.The structures of intrinsically disordered proteins (IDPs) are highly dynamic. It is hard to characterize the structures of these proteins experimentally. Molecular dynamics (MD) simulation is a powerful tool in the understanding of protein dynamic structures and function. This chapter describes the application of metadynamics-based enhanced sampling methods in the study of phosphorylation regulation on the structure of kinase-inducible domains (KID). The structural properties of free pKID and KID were obtained by parallel tempering metadynamics combined with well-tempered ensemble (PTMetaD WTE) method, and the binding free energy surfaces of pKID/KID and KIX were characterized by bias-exchanged metadynamics (BE-MetaD) simulations.Molecular dynamics simulations can in theory reveal the thermodynamics and kinetics of peptide conformational transitions at atomic-level resolution. However, even with modern computing power, they are limited in the timescales they can sample, which is especially problematic for peptides that are fully or partially disordered. Here, we discuss how the enhanced sampling methods accelerated molecular dynamics (aMD) and metadynamics can be leveraged in a complementary fashion to quickly explore conformational space and then robustly quantify the underlying free energy landscape. We apply these methods to two peptides that have an intrinsically disordered nature, the histone H3 and H4 N-terminal tails, and use metadynamics to compute the free energy landscape along collective variables discerned from aMD simulations. Results show that these peptides are largely disordered, with a slight preference for α-helical structures.The amphipathic α-helix is a common motif for peptide adsorption to membranes. Many physiologically relevant events involving membrane-adsorbed peptides occur over time and size scales readily accessible to coarse-grain molecular dynamics simulations. This methodological suitability, however, comes with a number of pitfalls. Here, I exemplify a multi-step adsorption equilibration procedure on the antimicrobial peptide Magainin 2. It involves careful control of peptide freedom to promote optimal membrane adsorption before other interactions are allowed. This shortens preparation times prior to production simulations while avoiding divergence into unrealistic or artifactual configurations.Understanding the interactions between peptides and lipid membranes could not only accelerate the development of antimicrobial peptides as treatments for infections but also be applied to finding targeted therapies for cancer and other diseases. However, designing biophysical experiments to study molecular interactions between flexible peptides and fluidic lipid membranes has been an ongoing challenge. Recently, with hardware advances, algorithm improvements, and more accurate parameterizations (i.e., force fields), all-atom molecular dynamics (MD) simulations have been used as a "computational microscope" to investigate the molecular interactions and mechanisms of membrane-active peptides in cell membranes (Chen et al., Curr Opin Struct Biol 61160-166, 2020; Ulmschneider and Ulmschneider, Acc Chem Res 51(5)1106-1116, 2018; Dror et al., Annu Rev Biophys 41429-452, 2012). In this chapter, we describe how to utilize MD simulations to predict and study peptide dynamics and how to validate the simulations by circular dichroism, intrinsic fluorescent probe, membrane leakage assay, electrical impedance, and isothermal titration calorimetry.